84.51° Overview84.51° is a retail data science, insights and media company. We help the Kroger company, consumer packaged goods companies, agencies, publishers and affiliated partners create more personalized and valuable experiences for shoppers across the path to purchase.
Powered by cutting edge science, we leverage 1st party retail data from nearly 1 of 2 US households and 2BN+ transactions to fuel a more customer-centric journey utilizing 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.
Join us at 84.51°!
Job Title: Lead Data Scientist - Multiple positions available
Job Location: 100 W. 5th Street, Cincinnati, OH 45202 and various unanticipated worksites in U.S.
Responsibilities- Engage with internal or external stakeholders to translate business questions into statistical and machine learning paradigms.
- Create innovative solutions using supervised and unsupervised learning techniques.
- Identify appropriate models and technologies to create analytical business solutions.
- Develop methods that use A/B testing, Champion/Challenger, design of experiments to improve customer experience.
- Collaborate with data and software engineers to deploy and automate data science solutions.
- Leverage statistical programming languages and packages with an emphasis on open source software such as R, Python and other big data technologies.
- Use data visualization tools to deliver customer insights to stakeholders.
- Adhere to stringent quality assurance and documentation standards using version control such as git, markdown, and Jupyter.
Basic Qualifications- Master’s degree or higher (or foreign educational equivalent) in Data Science or a closely related quantitative field such as Statistics, Mathematics, Computer Science, or Business Analytics.
- At least two years of experience in all of the following, gained at any time:
- Developing analytical solutions using statistical methods and machine learning algorithms.
- Querying data from large multi-terabyte customer data sets using software querying languages.
- Using R, Python, or other big data statistical software to develop analytical solutions.
- Performing data wrangling, data cleaning and prep, dimensionality reduction.
- Creating computationally efficient solutions.
- Utilizing data visualization skills and presenting technical solutions to non-technical audiences.
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